Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "239" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 25 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 25 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460008 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.285654 | -0.925517 | -0.222293 | 0.137222 | -0.145172 | -0.059215 | -0.680210 | -0.165738 | 0.6360 | 0.6461 | 0.3267 | nan | nan |
| 2460007 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.841091 | -0.673932 | 0.238629 | 0.270974 | -0.287368 | -0.414706 | -0.680511 | 0.477846 | 0.6063 | 0.6177 | 0.3446 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5802 | 0.6033 | 0.3243 | nan | nan |
| 2459998 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.824571 | -0.808305 | -0.256907 | -0.029079 | -0.518097 | -0.741572 | -0.144957 | 0.327153 | 0.5885 | 0.6043 | 0.3801 | nan | nan |
| 2459997 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.858612 | -0.881808 | -0.021050 | 0.117403 | -0.536254 | -0.939804 | 0.040102 | 1.057651 | 0.5983 | 0.6163 | 0.3851 | nan | nan |
| 2459996 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.944304 | -0.340702 | -0.308127 | 0.368212 | -0.463704 | -0.444094 | 2.435809 | 2.266051 | 0.6116 | 0.6262 | 0.3952 | nan | nan |
| 2459995 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.957145 | -0.858856 | -0.210865 | 0.187460 | -0.395064 | -0.102744 | 1.767865 | 3.039570 | 0.6035 | 0.6208 | 0.3856 | nan | nan |
| 2459994 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.924608 | -1.426886 | -0.078189 | -0.055125 | -0.533066 | -0.787934 | 0.548342 | 2.101214 | 0.5995 | 0.6146 | 0.3798 | nan | nan |
| 2459993 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 249.527816 | 249.211020 | inf | inf | 2341.883609 | 2328.074717 | 5481.116884 | 5510.095765 | nan | nan | nan | nan | nan |
| 2459991 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.969043 | -1.428249 | 0.090518 | 0.173041 | -0.638213 | -0.747742 | 1.607234 | 1.105415 | 0.5994 | 0.6061 | 0.3885 | nan | nan |
| 2459990 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.790033 | -1.091970 | 0.094138 | 0.221582 | -0.677701 | -0.891068 | 2.670533 | 1.266278 | 0.5985 | 0.6089 | 0.3859 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.907021 | -1.156749 | 0.221356 | 0.144147 | -0.597775 | -0.888869 | 5.146638 | 0.781561 | 0.5957 | 0.6118 | 0.3863 | nan | nan |
| 2459988 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.538486 | -0.512409 | 0.584659 | 0.734154 | -0.614536 | -0.689240 | 0.468608 | 0.156441 | 0.5971 | 0.6120 | 0.3773 | nan | nan |
| 2459987 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.097753 | -1.310431 | -0.037075 | 0.006085 | -0.555247 | -0.470340 | 0.353368 | 1.640042 | 0.6033 | 0.6164 | 0.3790 | nan | nan |
| 2459986 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.851108 | -1.232656 | -0.014330 | 0.154558 | -0.726053 | -0.702570 | -0.236128 | -0.006545 | 0.6204 | 0.6380 | 0.3472 | nan | nan |
| 2459985 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.587099 | -0.449785 | 0.333312 | 0.515699 | 0.018191 | -0.894148 | -0.327810 | -0.472205 | 0.6059 | 0.6192 | 0.3847 | nan | nan |
| 2459984 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.851311 | -1.097001 | -0.197644 | 0.233814 | -0.705016 | -0.532129 | 0.257540 | 0.717469 | 0.6215 | 0.6377 | 0.3637 | nan | nan |
| 2459983 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.927614 | -1.195696 | -0.005744 | 0.301463 | -0.681228 | -0.352424 | -0.466994 | 0.084071 | 0.6262 | 0.6499 | 0.3332 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.681123 | -0.843007 | -0.110139 | 0.014944 | -0.964974 | -0.720880 | -0.703537 | -0.845272 | 0.6792 | 0.6751 | 0.2965 | nan | nan |
| 2459981 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.807168 | -1.228992 | 0.240229 | 0.338551 | 1.794690 | -0.888253 | 14.918172 | 1.232610 | 0.5980 | 0.6196 | 0.3818 | nan | nan |
| 2459980 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.940265 | -1.011646 | 0.304722 | 0.338996 | -0.378800 | -1.345182 | -0.233258 | -0.410473 | 0.6435 | 0.6522 | 0.3157 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.002603 | -1.510145 | -0.116260 | -0.167763 | -0.479852 | -0.850688 | 4.591060 | 1.290672 | 0.5953 | 0.6143 | 0.3828 | nan | nan |
| 2459978 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.079070 | -1.455355 | -0.139363 | 0.027655 | -0.546150 | -1.141242 | 4.578635 | 1.646189 | 0.5951 | 0.6134 | 0.3901 | nan | nan |
| 2459977 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.838157 | -1.206380 | -0.166803 | -0.116770 | -0.806167 | -1.184203 | 1.172752 | 1.613815 | 0.5595 | 0.5762 | 0.3488 | nan | nan |
| 2459976 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.829037 | -1.303494 | -0.102001 | -0.019634 | -0.397539 | -0.669349 | 1.076934 | 1.515408 | 0.6033 | 0.6191 | 0.3796 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Power | 0.137222 | -0.925517 | -1.285654 | 0.137222 | -0.222293 | -0.059215 | -0.145172 | -0.165738 | -0.680210 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 0.477846 | -0.841091 | -0.673932 | 0.238629 | 0.270974 | -0.287368 | -0.414706 | -0.680511 | 0.477846 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 0.327153 | -0.824571 | -0.808305 | -0.256907 | -0.029079 | -0.518097 | -0.741572 | -0.144957 | 0.327153 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 1.057651 | -0.858612 | -0.881808 | -0.021050 | 0.117403 | -0.536254 | -0.939804 | 0.040102 | 1.057651 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Temporal Discontinuties | 2.435809 | -0.944304 | -0.340702 | -0.308127 | 0.368212 | -0.463704 | -0.444094 | 2.435809 | 2.266051 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 3.039570 | -0.957145 | -0.858856 | -0.210865 | 0.187460 | -0.395064 | -0.102744 | 1.767865 | 3.039570 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 2.101214 | -0.924608 | -1.426886 | -0.078189 | -0.055125 | -0.533066 | -0.787934 | 0.548342 | 2.101214 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Power | inf | 249.527816 | 249.211020 | inf | inf | 2341.883609 | 2328.074717 | 5481.116884 | 5510.095765 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Temporal Discontinuties | 1.607234 | -0.969043 | -1.428249 | 0.090518 | 0.173041 | -0.638213 | -0.747742 | 1.607234 | 1.105415 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Temporal Discontinuties | 2.670533 | -1.091970 | -0.790033 | 0.221582 | 0.094138 | -0.891068 | -0.677701 | 1.266278 | 2.670533 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Temporal Discontinuties | 5.146638 | -1.156749 | -0.907021 | 0.144147 | 0.221356 | -0.888869 | -0.597775 | 0.781561 | 5.146638 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Power | 0.734154 | -0.512409 | -0.538486 | 0.734154 | 0.584659 | -0.689240 | -0.614536 | 0.156441 | 0.468608 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 1.640042 | -1.097753 | -1.310431 | -0.037075 | 0.006085 | -0.555247 | -0.470340 | 0.353368 | 1.640042 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Power | 0.154558 | -1.232656 | -0.851108 | 0.154558 | -0.014330 | -0.702570 | -0.726053 | -0.006545 | -0.236128 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Power | 0.515699 | -0.449785 | -0.587099 | 0.515699 | 0.333312 | -0.894148 | 0.018191 | -0.472205 | -0.327810 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 0.717469 | -0.851311 | -1.097001 | -0.197644 | 0.233814 | -0.705016 | -0.532129 | 0.257540 | 0.717469 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Power | 0.301463 | -0.927614 | -1.195696 | -0.005744 | 0.301463 | -0.681228 | -0.352424 | -0.466994 | 0.084071 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Power | 0.014944 | -0.681123 | -0.843007 | -0.110139 | 0.014944 | -0.964974 | -0.720880 | -0.703537 | -0.845272 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Temporal Discontinuties | 14.918172 | -1.228992 | -0.807168 | 0.338551 | 0.240229 | -0.888253 | 1.794690 | 1.232610 | 14.918172 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Power | 0.338996 | -1.011646 | -0.940265 | 0.338996 | 0.304722 | -1.345182 | -0.378800 | -0.410473 | -0.233258 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Temporal Discontinuties | 4.591060 | -1.002603 | -1.510145 | -0.116260 | -0.167763 | -0.479852 | -0.850688 | 4.591060 | 1.290672 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | ee Temporal Discontinuties | 4.578635 | -1.455355 | -1.079070 | 0.027655 | -0.139363 | -1.141242 | -0.546150 | 1.646189 | 4.578635 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 1.613815 | -0.838157 | -1.206380 | -0.166803 | -0.116770 | -0.806167 | -1.184203 | 1.172752 | 1.613815 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | N18 | RF_ok | nn Temporal Discontinuties | 1.515408 | -1.303494 | -0.829037 | -0.019634 | -0.102001 | -0.669349 | -0.397539 | 1.515408 | 1.076934 |